DOMAIN: Health Care • CONTEXT: Computer vision can be used in health care for identifying diseases. In Pneumonia detection we need to detect Inflammation of the lungs. In this challenge, you’re required to build an algorithm to detect a visual signal for pneumonia in medical images. Specifically, your algorithm needs to automatically locate lung opacities on chest radiographs. • DATA DESCRIPTION:
- In the dataset, some of the features are labeled “Not Normal No Lung Opacity”. This extra third class indicates that while pneumonia was determined not to be present, there was nonetheless some type of abnormality on the image and oftentimes this finding may mimic the appearance of true pneumonia. Dicom original images: - Medical images are stored in a special format called DICOM files (*.dcm). They contain a combination of header metadata as well as underlying raw image arrays for pixel data.
- Dataset has been attached along with this project. Please use the same for this capstone project.
Original link to the dataset : https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data [ for your reference only ]. You can refer to the details of the dataset in the above link
PROJECT OBJECTIVE: Design a DL based algorithm for detecting pneumonia. • PROJECT TASK: [ Score: 100 points]
- Milestone 1: [ Score: 40 points]
*Input: Context and Dataset Process:
Step 1: Import the data. [ 3 points ]
Step 2: Map training and testing images to its classes. [ 4 points ]
Step 3: Map training and testing images to its annotations. [ 4 points ]
Step 4: Preprocessing and Visualisation of different classes [4 Points]
Step 5: Display images with bounding box. [ 5 points ]
Step 6: Design, train and test basic CNN models for classification. [ 10 points ]
Step 7: Interim report [ 10 points ]
Submission: Interim report, Jupyter Notebook with all the steps in Milestone-1
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import pandas as pd
import os
import zipfile
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import math
import numpy as np
import seaborn as sns
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!pip install pydicom
Collecting pydicom Downloading pydicom-3.0.1-py3-none-any.whl.metadata (9.4 kB) Downloading pydicom-3.0.1-py3-none-any.whl (2.4 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.4/2.4 MB 22.6 MB/s eta 0:00:00 Installing collected packages: pydicom Successfully installed pydicom-3.0.1
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import pydicom as dcm
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# Step 1: Install Kaggle API Client
!pip install kaggle
Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.6.17) Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0) Requirement already satisfied: certifi>=2023.7.22 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2024.8.30) Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2) Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.32.3) Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.66.6) Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.4) Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.2.3) Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.2.0) Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1) Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3) Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.4.0) Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.10)
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# Upload the kaggle.json file
from google.colab import files
files.upload() # This will prompt you to upload the kaggle.json file. please upload your json file. this key is belong to me
Saving kaggle.json to kaggle.json
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{'kaggle.json': b'{"username":"vishjani","key":"268c8a10232dfe9f51f6078fcf084e6b"}'}
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# Set up Kaggle API configuration
# Create the .kaggle folder and set proper permissions
os.makedirs("/root/.kaggle", exist_ok=True)
os.rename("kaggle.json", "/root/.kaggle/kaggle.json")
os.chmod("/root/.kaggle/kaggle.json", 600)
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# Download the RSNA dataset
#use your google drive or physical path of your computer
!kaggle competitions download -c rsna-pneumonia-detection-challenge -p /content/drive/MyDrive/Vishal_Notebook/rsna-pneumonia-detection-challenge
Downloading rsna-pneumonia-detection-challenge.zip to /content/drive/MyDrive/Vishal_Notebook/rsna-pneumonia-detection-challenge 99% 3.64G/3.66G [00:44<00:00, 115MB/s] 100% 3.66G/3.66G [00:44<00:00, 88.4MB/s]
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# Unzip the dataset
#use your google drive or physical path of your computer
# Specify the path to the downloaded zip file
zip_file_path = "/content/drive/MyDrive/Vishal_Notebook/rsna-pneumonia-detection-challenge/rsna-pneumonia-detection-challenge.zip"
# Specify the directory to extract the files to
extract_dir = "/content/drive/MyDrive/Vishal_Notebook/rsna-pneumonia-detection-challenge"
# Create the extraction directory if it doesn't exist
os.makedirs(extract_dir, exist_ok=True)
# Unzip the dataset
with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
zip_ref.extractall(extract_dir)
print("Dataset unzipped successfully!")
Dataset unzipped successfully!
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# Verify the dataset files
#use your google drive or physical path of your computer
# Specify the directory containing the dataset files
data_dir = "/content/drive/MyDrive/Vishal_Notebook/rsna-pneumonia-detection-challenge"
# List all files and directories within the data directory
for root, dirs, files in os.walk(data_dir):
for file in files:
print(os.path.join(root, file))
Streaming output truncated to the last 5000 lines.
/content/drive/MyDrive/Vishal_Notebook/rsna-pneumonia-detection-challenge/stage_2_train_images/36d55e8a-0fa3-4af7-91e7-725bbdbd442e.dcm
/content/drive/MyDrive/Vishal_Notebook/rsna-pneumonia-detection-challenge/stage_2_train_images/e6124983-5035-4478-9429-7b04c5de614b.dcm
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/content/drive/MyDrive/Vishal_Notebook/rsna-pneumonia-detection-challenge/stage_2_train_images/95cadf88-afc4-445b-8198-e8af982a9294.dcm
/content/drive/MyDrive/Vishal_Notebook/rsna-pneumonia-detection-challenge/stage_2_train_images/ed282a1c-7989-4924-9209-61d3e6b30785.dcm
/content/drive/MyDrive/Vishal_Notebook/rsna-pneumonia-detection-challenge/stage_2_train_images/695040a0-f7dd-46b4-8eb5-5dc87290e43b.dcm
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/content/drive/MyDrive/Vishal_Notebook/rsna-pneumonia-detection-challenge/stage_2_train_images/b3a7782d-13d4-417f-b897-67e873c757c7.dcm
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In [ ]:
#Step 2: Map training and testing images to its classes
# use your google drive or physical path of your computer
# Load the training and testing CSV files
train_df = pd.read_csv("/content/drive/MyDrive/Vishal_Notebook/rsna-pneumonia-detection-challenge/stage_2_train_labels.csv")
test_df = pd.read_csv("/content/drive/MyDrive/Vishal_Notebook/rsna-pneumonia-detection-challenge/stage_2_sample_submission.csv")
# Create mappings for training images
train_image_class_map = {}
for index, row in train_df.iterrows():
patient_id = row['patientId']
target = row['Target']
train_image_class_map[patient_id] = target
# Create mappings for testing images
test_image_class_map = {}
for index, row in test_df.iterrows():
patient_id = row['patientId']
# Placeholder for test targets (actual targets are not available in sample submission)
test_image_class_map[patient_id] = 0 # or any default value
print("Training Image to Class Mapping:", list(train_image_class_map.items())[:5]) # Print first 5 items
print("Test Image to Class Mapping:", list(test_image_class_map.items())[:5]) # Print first 5 items
Training Image to Class Mapping: [('0004cfab-14fd-4e49-80ba-63a80b6bddd6', 0), ('00313ee0-9eaa-42f4-b0ab-c148ed3241cd', 0), ('00322d4d-1c29-4943-afc9-b6754be640eb', 0), ('003d8fa0-6bf1-40ed-b54c-ac657f8495c5', 0), ('00436515-870c-4b36-a041-de91049b9ab4', 1)]
Test Image to Class Mapping: [('0000a175-0e68-4ca4-b1af-167204a7e0bc', 0), ('0005d3cc-3c3f-40b9-93c3-46231c3eb813', 0), ('000686d7-f4fc-448d-97a0-44fa9c5d3aa6', 0), ('000e3a7d-c0ca-4349-bb26-5af2d8993c3d', 0), ('00100a24-854d-423d-a092-edcf6179e061', 0)]
In [ ]:
#Step 3: Map training and testing images to its annotations.
train_bbox_df = pd.read_csv("/content/drive/MyDrive/Vishal_Notebook/rsna-pneumonia-detection-challenge/stage_2_train_labels.csv")
train_bbox_df.head()
Out[ ]:
| patientId | x | y | width | height | Target | |
|---|---|---|---|---|---|---|
| 0 | 0004cfab-14fd-4e49-80ba-63a80b6bddd6 | NaN | NaN | NaN | NaN | 0 |
| 1 | 00313ee0-9eaa-42f4-b0ab-c148ed3241cd | NaN | NaN | NaN | NaN | 0 |
| 2 | 00322d4d-1c29-4943-afc9-b6754be640eb | NaN | NaN | NaN | NaN | 0 |
| 3 | 003d8fa0-6bf1-40ed-b54c-ac657f8495c5 | NaN | NaN | NaN | NaN | 0 |
| 4 | 00436515-870c-4b36-a041-de91049b9ab4 | 264.0 | 152.0 | 213.0 | 379.0 | 1 |
In [ ]:
train_bbox_df.shape
Out[ ]:
(30227, 6)
In [ ]:
train_bbox_df[train_bbox_df.isnull().any(axis=1)].Target.value_counts()
Out[ ]:
| count | |
|---|---|
| Target | |
| 0 | 20672 |
In [ ]:
train_bbox_df[~train_bbox_df.isnull().any(axis=1)].Target.value_counts()
Out[ ]:
| count | |
|---|---|
| Target | |
| 1 | 9555 |
In [ ]:
train_bbox_df.Target.value_counts()
Out[ ]:
| count | |
|---|---|
| Target | |
| 0 | 20672 |
| 1 | 9555 |
In [ ]:
label_count=train_bbox_df['Target'].value_counts()
explode = (0.01,0.01)
fig1, ax1 = plt.subplots(figsize=(5,5))
ax1.pie(label_count.values, explode=explode, labels=label_count.index, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal')
plt.title('Target Distribution')
plt.show()
print("Any Unique records exist in PatientID Column:" ,train_bbox_df['patientId'].is_unique)
Any Unique records exist in PatientID Column: False
In [ ]:
duplicate = train_bbox_df[train_bbox_df.duplicated(['patientId'])]
print("Duplicate Rows based on PatientID :", duplicate.patientId.count())
# Print the resultant Dataframe
duplicate
Duplicate Rows based on PatientID : 3543
Out[ ]:
| patientId | x | y | width | height | Target | |
|---|---|---|---|---|---|---|
| 5 | 00436515-870c-4b36-a041-de91049b9ab4 | 562.0 | 152.0 | 256.0 | 453.0 | 1 |
| 9 | 00704310-78a8-4b38-8475-49f4573b2dbb | 695.0 | 575.0 | 162.0 | 137.0 | 1 |
| 15 | 00aecb01-a116-45a2-956c-08d2fa55433f | 547.0 | 299.0 | 119.0 | 165.0 | 1 |
| 17 | 00c0b293-48e7-4e16-ac76-9269ba535a62 | 650.0 | 511.0 | 206.0 | 284.0 | 1 |
| 20 | 00f08de1-517e-4652-a04f-d1dc9ee48593 | 571.0 | 275.0 | 230.0 | 476.0 | 1 |
| ... | ... | ... | ... | ... | ... | ... |
| 30209 | c18d1138-ba74-4af5-af21-bdd4d2c96bb5 | 295.0 | 473.0 | 133.0 | 176.0 | 1 |
| 30215 | c1cddf32-b957-4753-acaa-472ab1447e86 | 766.0 | 475.0 | 135.0 | 213.0 | 1 |
| 30220 | c1e73a4e-7afe-4ec5-8af6-ce8315d7a2f2 | 316.0 | 504.0 | 179.0 | 273.0 | 1 |
| 30222 | c1ec14ff-f6d7-4b38-b0cb-fe07041cbdc8 | 185.0 | 298.0 | 228.0 | 379.0 | 1 |
| 30226 | c1f7889a-9ea9-4acb-b64c-b737c929599a | 233.0 | 424.0 | 201.0 | 356.0 | 1 |
3543 rows × 6 columns
In [ ]:
Class_labels = pd.read_csv('/content/drive/MyDrive/Vishal_Notebook/rsna-pneumonia-detection-challenge/stage_2_detailed_class_info.csv')
Class_labels.shape
Out[ ]:
(30227, 2)
In [ ]:
Class_labels.head()
Out[ ]:
| patientId | class | |
|---|---|---|
| 0 | 0004cfab-14fd-4e49-80ba-63a80b6bddd6 | No Lung Opacity / Not Normal |
| 1 | 00313ee0-9eaa-42f4-b0ab-c148ed3241cd | No Lung Opacity / Not Normal |
| 2 | 00322d4d-1c29-4943-afc9-b6754be640eb | No Lung Opacity / Not Normal |
| 3 | 003d8fa0-6bf1-40ed-b54c-ac657f8495c5 | Normal |
| 4 | 00436515-870c-4b36-a041-de91049b9ab4 | Lung Opacity |
In [ ]:
Class_labels.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 30227 entries, 0 to 30226 Data columns (total 2 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 patientId 30227 non-null object 1 class 30227 non-null object dtypes: object(2) memory usage: 472.4+ KB
In [ ]:
Class_labels['class'].value_counts()
Out[ ]:
| count | |
|---|---|
| class | |
| No Lung Opacity / Not Normal | 11821 |
| Lung Opacity | 9555 |
| Normal | 8851 |
In [ ]:
label_count=Class_labels['class'].value_counts()
explode = (0.01,0.01,0.01)
fig1, ax1 = plt.subplots(figsize=(5,5))
ax1.pie(label_count.values, explode=explode, labels=label_count.index, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal')
plt.title('Class Distribution')
plt.show()
Each patientId corresponds to a unique image class as per below depending what is the current state of the patient's lung as per below,
- 'No Lung Opacity / Not Normal'
- 'Normal'
- 'Lung Opacity'.
In [ ]:
def get_feature_distribution(data, feature):
# Count for each label
label_counts = data[feature].value_counts()
# Count the number of items in each class
total_samples = len(data)
print("Feature: {}".format(feature))
for i in range(len(label_counts)):
label = label_counts.index[i]
count = label_counts.values[i]
percent = int((count / total_samples) * 10000) / 100
print("{:<30s}: {} which is {}% of the total data in the dataset".format(label, count, percent))
In [ ]:
get_feature_distribution(Class_labels, 'class')
Feature: class No Lung Opacity / Not Normal : 11821 which is 39.1% of the total data in the dataset Lung Opacity : 9555 which is 31.61% of the total data in the dataset Normal : 8851 which is 29.28% of the total data in the dataset
In [ ]:
duplicateClassRowsDF = Class_labels[Class_labels.duplicated(['patientId'])]
duplicateClassRowsDF.shape
print('No of dulicate rows: ', duplicateClassRowsDF.shape[0])
No of dulicate rows: 3543
In [ ]:
duplicateClassRowsDF.head(5)
Out[ ]:
| patientId | class | |
|---|---|---|
| 5 | 00436515-870c-4b36-a041-de91049b9ab4 | Lung Opacity |
| 9 | 00704310-78a8-4b38-8475-49f4573b2dbb | Lung Opacity |
| 15 | 00aecb01-a116-45a2-956c-08d2fa55433f | Lung Opacity |
| 17 | 00c0b293-48e7-4e16-ac76-9269ba535a62 | Lung Opacity |
| 20 | 00f08de1-517e-4652-a04f-d1dc9ee48593 | Lung Opacity |
In [ ]:
Class_labels[Class_labels.patientId=='00c0b293-48e7-4e16-ac76-9269ba535a62']
Out[ ]:
| patientId | class | |
|---|---|---|
| 16 | 00c0b293-48e7-4e16-ac76-9269ba535a62 | Lung Opacity |
| 17 | 00c0b293-48e7-4e16-ac76-9269ba535a62 | Lung Opacity |
In [ ]:
#Step 3: Map training and testing images to its annotations.
# Load the bounding box annotations
#train_bbox_df = pd.read_csv("/content/drive/MyDrive/Vishal_Notebook/rsna-pneumonia-detection-challenge/stage_2_train_labels.csv")
# Create mappings for training images to bounding boxes
train_image_bbox_map = {}
for index, row in train_bbox_df.iterrows():
patient_id = row['patientId']
x_min = row['x']
y_min = row['y']
width = row['width']
height = row['height']
# Check for NaN values in bounding box coordinates
if pd.isna(x_min) or pd.isna(y_min) or pd.isna(width) or pd.isna(height):
continue # Skip rows with missing bounding box information
# Store the bounding box information as a tuple (x_min, y_min, width, height)
if patient_id not in train_image_bbox_map:
train_image_bbox_map[patient_id] = []
train_image_bbox_map[patient_id].append((x_min, y_min, width, height))
# Example usage: Accessing bounding boxes for a particular image
# Print the first 5 patient IDs and their associated bounding boxes
for patient_id, bboxes in list(train_image_bbox_map.items())[:5]:
print(f"Patient ID: {patient_id}, Bounding Boxes: {bboxes}")
# Note: Test images do not have corresponding bounding box annotations in the sample submission file
# Thus, there is no mapping to create for test images in this step.
Patient ID: 00436515-870c-4b36-a041-de91049b9ab4, Bounding Boxes: [(264.0, 152.0, 213.0, 379.0), (562.0, 152.0, 256.0, 453.0)] Patient ID: 00704310-78a8-4b38-8475-49f4573b2dbb, Bounding Boxes: [(323.0, 577.0, 160.0, 104.0), (695.0, 575.0, 162.0, 137.0)] Patient ID: 00aecb01-a116-45a2-956c-08d2fa55433f, Bounding Boxes: [(288.0, 322.0, 94.0, 135.0), (547.0, 299.0, 119.0, 165.0)] Patient ID: 00c0b293-48e7-4e16-ac76-9269ba535a62, Bounding Boxes: [(306.0, 544.0, 168.0, 244.0), (650.0, 511.0, 206.0, 284.0)] Patient ID: 00f08de1-517e-4652-a04f-d1dc9ee48593, Bounding Boxes: [(181.0, 184.0, 206.0, 506.0), (571.0, 275.0, 230.0, 476.0)]
In [ ]:
train_df.head(5)
# Merging the Dataset - 'labels' and 'class_labels':
training_data = pd.concat([train_df, Class_labels['class']], axis = 1)
training_data.head()
Out[ ]:
| patientId | x | y | width | height | Target | class | |
|---|---|---|---|---|---|---|---|
| 0 | 0004cfab-14fd-4e49-80ba-63a80b6bddd6 | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal |
| 1 | 00313ee0-9eaa-42f4-b0ab-c148ed3241cd | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal |
| 2 | 00322d4d-1c29-4943-afc9-b6754be640eb | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal |
| 3 | 003d8fa0-6bf1-40ed-b54c-ac657f8495c5 | NaN | NaN | NaN | NaN | 0 | Normal |
| 4 | 00436515-870c-4b36-a041-de91049b9ab4 | 264.0 | 152.0 | 213.0 | 379.0 | 1 | Lung Opacity |
In [ ]:
fig, ax = plt.subplots(nrows = 1, figsize = (12, 6))
temp = training_data.groupby('Target')['class'].value_counts()
data_target_class = pd.DataFrame(data = {'Values': temp.values}, index = temp.index).reset_index()
sns.barplot(ax = ax, x = 'Target', y = 'Values', hue = 'class', data = data_target_class, palette = 'Set3')
plt.title('Class and Target Distrubution')
Out[ ]:
Text(0.5, 1.0, 'Class and Target Distrubution')
In [ ]:
# Number of duplicates in patients:
print('Number of unique patientId are: {}'.format(Class_labels['patientId'].nunique()))
Number of unique patientId are: 26684
In [ ]:
# import pydicom as dcm
# def inspectImages(data):
# img_data = list(data.T.to_dict().values())
# f, ax = plt.subplots(3,3, figsize=(16,18))
# for i,data_row in enumerate(img_data):
# patientImage = data_row['patientId']
# # dcm_file = '/content/stage_2_train_images/'+'{}.dcm'.format(patientImage)
# dcm_file = '/content/drive/MyDrive/Vishal_Notebook/rsna-pneumonia-detection-challenge/stage_2_train_images/'+'{}.dcm'.format(patientImage)
# # Use dcmread instead of read_file
# data_row_img_data = dcm.dcmread(dcm_file)
# modality = data_row_img_data.Modality
# age = data_row_img_data.PatientAge
# sex = data_row_img_data.PatientSex
# data_row_img = dcm.dcmread(dcm_file)
# ax[i//3, i%3].imshow(data_row_img.pixel_array, cmap=plt.cm.bone)
# ax[i//3, i%3].axis('off')
# ax[i//3, i%3].set_title('ID: {}\nModality: {} Age: {} Sex: {} Target: {}\nClass: {}\Bounds: {}:{}:{}:{}'.format(
# data_row['patientId'],
# modality, age, sex, data_row['Target'], data_row['class'],
# data_row['x'],data_row['y'],data_row['width'],data_row['height']))
# label = data_row["class"]
# if not math.isnan(data_row['x']):
# x, y, width, height = data_row['x'],data_row['y'],data_row['width'],data_row['height']
# rect = patches.Rectangle((x, y),width, height,
# linewidth = 2,
# edgecolor = 'r',
# facecolor = 'none')
# # Draw the bounding box on top of the image
# ax[i//3, i%3].add_patch(rect)
# plt.show()
# # Function to preprocess and visualize images
# def preprocess_and_visualize(image_path, target):
# # Load DICOM image
# ds = dcm.dcmread(image_path)
# image = ds.pixel_array
# # Normalize pixel values to [0, 1]
# image = image / 255.0
# # Display the image
# plt.figure(figsize=(6, 6))
# plt.imshow(image, cmap=plt.cm.gray)
# plt.title(f"Image Class: {target}")
# plt.axis('off')
# plt.show()
# # Function to preprocess and visualize images
# def preprocess_and_visualize(image_path, target):
# # Load DICOM image
# ds = dcm.dcmread(image_path)
# image = ds.pixel_array
# # Normalize pixel values to [0, 1]
# image = image / 255.0
# # Display the image
# plt.figure(figsize=(6, 6))
# plt.imshow(image, cmap=plt.cm.gray)
# plt.title(f"Image Class: {target}")
# plt.axis('off')
# plt.show()
In [ ]:
# sample_patient_ids = list(train_image_class_map.keys())[:5]
# # Replace with the actual path to the images
# image_dir = "/content/drive/MyDrive/Vishal_Notebook/rsna-pneumonia-detection-challenge/stage_2_train_images"
# for patient_id in sample_patient_ids:
# image_path = os.path.join(image_dir, f"{patient_id}.dcm")
# if os.path.exists(image_path):
# target = train_image_class_map[patient_id]
# preprocess_and_visualize(image_path, target)
# else:
# print(f"Image not found for patient ID: {patient_id}")
In [ ]:
#view images with target is 1
# inspectImages(training_data[training_data['Target']==1].sample(9))
In [ ]:
#view images with target is 0
# inspectImages(training_data[training_data['Target']==0].sample(9))
In [ ]:
#check duplicate and remove
one = training_data[training_data.Target == 1].drop_duplicates('patientId').shape[0]
zero = training_data[training_data.Target == 0].drop_duplicates('patientId').shape[0]
total = training_data.drop_duplicates('patientId').shape[0]
In [ ]:
print(f'No of entries which has Pneumonia: {one} i.e., {round(one/total*100, 0)}%')
print(f'No of entries which don\'t have Pneumonia: {zero} i.e., {round(zero/total, 0)}%')
_ = training_data.drop_duplicates('patientId').drop_duplicates('patientId')['Target'].value_counts().plot(kind = 'pie', autopct = '%.0f%%', labels = ['Negative', 'Positive'], figsize = (10, 6))
No of entries which has Pneumonia: 6012 i.e., 23.0% No of entries which don't have Pneumonia: 20672 i.e., 1.0%
In [ ]:
print('Number of nulls in bounding box columns: {}'.format(training_data[['x', 'y', 'width', 'height']].isnull().sum().to_dict()))
Number of nulls in bounding box columns: {'x': 20672, 'y': 20672, 'width': 20672, 'height': 20672}
In [ ]:
training_data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 30227 entries, 0 to 30226 Data columns (total 7 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 patientId 30227 non-null object 1 x 9555 non-null float64 2 y 9555 non-null float64 3 width 9555 non-null float64 4 height 9555 non-null float64 5 Target 30227 non-null int64 6 class 30227 non-null object dtypes: float64(4), int64(1), object(2) memory usage: 1.6+ MB
In [ ]:
bounding_box = training_data.groupby('patientId').size().to_frame('number_of_boxes').reset_index()
train_labels = training_data.merge(bounding_box, on = 'patientId', how = 'left')
print('Number of patientIds per bounding box in the dataset: ')
(bounding_box.groupby('number_of_boxes').size().to_frame('number_of_patientId').reset_index().set_index('number_of_boxes').sort_values(by = 'number_of_boxes'))
Number of patientIds per bounding box in the dataset:
Out[ ]:
| number_of_patientId | |
|---|---|
| number_of_boxes | |
| 1 | 23286 |
| 2 | 3266 |
| 3 | 119 |
| 4 | 13 |
Thus, there are 23286 unique patients which have only one entry in the dataset.
There are bounding boxs in the dataset are as per below,
- 3266 with 2 bounding box
- 119 with 3 bounding box
- 13 with 4 bounding box coordinates.
In [ ]:
# Merging the two dataset - 'train_labels' and 'class_labels':
training_data = pd.concat([train_labels], axis = 1)
print('After merging, the dataset looks like: \n')
training_data.head()
After merging, the dataset looks like:
Out[ ]:
| patientId | x | y | width | height | Target | class | number_of_boxes | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0004cfab-14fd-4e49-80ba-63a80b6bddd6 | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal | 1 |
| 1 | 00313ee0-9eaa-42f4-b0ab-c148ed3241cd | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal | 1 |
| 2 | 00322d4d-1c29-4943-afc9-b6754be640eb | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal | 1 |
| 3 | 003d8fa0-6bf1-40ed-b54c-ac657f8495c5 | NaN | NaN | NaN | NaN | 0 | Normal | 1 |
| 4 | 00436515-870c-4b36-a041-de91049b9ab4 | 264.0 | 152.0 | 213.0 | 379.0 | 1 | Lung Opacity | 2 |
In [ ]:
print('After merge, the dataset has {} rows and {} columns.'.format(training_data.shape[0], training_data.shape[1]))
After merge, the dataset has 30227 rows and 8 columns.
In [ ]:
import pydicom as dcm
data_row_img_data=[]
def inspectImages(data):
img_data = list(data.T.to_dict().values())
f, ax = plt.subplots(3,3, figsize=(16,18))
for i,data_row in enumerate(img_data):
patientImage = data_row['patientId']
# dcm_file = '/content/stage_2_train_images/'+'{}.dcm'.format(patientImage)
dcm_file = '/content/drive/MyDrive/Vishal_Notebook/rsna-pneumonia-detection-challenge/stage_2_train_images/'+'{}.dcm'.format(patientImage)
# Use dcmread instead of read_file
data_row_img_data = dcm.dcmread(dcm_file)
modality = data_row_img_data.Modality
age = data_row_img_data.PatientAge
sex = data_row_img_data.PatientSex
data_row_img = dcm.dcmread(dcm_file)
ax[i//3, i%3].imshow(data_row_img.pixel_array, cmap=plt.cm.bone)
ax[i//3, i%3].axis('off')
ax[i//3, i%3].set_title('ID: {}\nModality: {} Age: {} Sex: {} Target: {}\nClass: {}\Bounds: {}:{}:{}:{}'.format(
data_row['patientId'],
modality, age, sex, data_row['Target'], data_row['class'],
data_row['x'],data_row['y'],data_row['width'],data_row['height']))
label = data_row["class"]
if not math.isnan(data_row['x']):
x, y, width, height = data_row['x'],data_row['y'],data_row['width'],data_row['height']
rect = patches.Rectangle((x, y),width, height,
linewidth = 2,
edgecolor = 'r',
facecolor = 'none')
# Draw the bounding box on top of the image
ax[i//3, i%3].add_patch(rect)
plt.show()
# Function to preprocess and visualize images
def preprocess_and_visualize(image_path, target):
# Load DICOM image
ds = dcm.dcmread(image_path)
image = ds.pixel_array
# Normalize pixel values to [0, 1]
image = image / 255.0
# Display the image
plt.figure(figsize=(6, 6))
plt.imshow(image, cmap=plt.cm.gray)
plt.title(f"Image Class: {target}")
plt.axis('off')
plt.show()
# Function to preprocess and visualize images
def preprocess_and_visualize(image_path, target):
# Load DICOM image
ds = dcm.dcmread(image_path)
image = ds.pixel_array
# Normalize pixel values to [0, 1]
image = image / 255.0
# Display the image
plt.figure(figsize=(6, 6))
plt.imshow(image, cmap=plt.cm.gray)
plt.title(f"Image Class: {target}")
plt.axis('off')
plt.show()
def get_feature_distribution(data, feature):
# Count for each label
label_counts = data[feature].value_counts()
# Count the number of items in each class
total_samples = len(data)
print("Feature: {}".format(feature))
for i in range(len(label_counts)):
label = label_counts.index[i]
count = label_counts.values[i]
percent = int((count / total_samples) * 10000) / 100
print("{:<30s}: {} which is {}% of the total data in the dataset".format(label, count, percent))
def show_dicom_images(data):
img_data = list(data.T.to_dict().values())
f, ax = plt.subplots(3,3, figsize=(16,18))
for i,data_row in enumerate(img_data):
patientImage = data_row['patientId']
dcm_file = '/content/drive/MyDrive/Vishal_Notebook/rsna-pneumonia-detection-challenge/stage_2_train_images/'+'{}.dcm'.format(patientImage)
# Use dcmread instead of read_file
data_row_img_data = dcm.dcmread(dcm_file)
modality = data_row_img_data.Modality
age = data_row_img_data.PatientAge
sex = data_row_img_data.PatientSex
data_row_img = dcm.dcmread(dcm_file)
ax[i//3, i%3].imshow(data_row_img.pixel_array, cmap=plt.cm.bone)
ax[i//3, i%3].axis('off')
ax[i//3, i%3].set_title('ID: {}\nModality: {} Age: {} Sex: {} Target: {}\nClass: {}\nPart: {}\nViewPosition: {}'.format(
data_row['patientId'],
modality, age, sex, data_row['Target'], data_row['class'],
data_row_img_data.BodyPartExamined, data_row_img_data.ViewPosition))
label = data_row["class"]
if not math.isnan(data_row['x']):
x, y, width, height = data_row['x'],data_row['y'],data_row['width'],data_row['height']
rect = patches.Rectangle((x, y),width, height,
linewidth = 2,
edgecolor = 'r',
facecolor = 'none')
# Draw the bounding box on top of the image
ax[i//3, i%3].add_patch(rect)
plt.show()
In [ ]:
# ds = dcm.dcmread(image_path)
# print("------------Diffrent Methods to read image data ------------");
# print("1. Read By Tuple (Tuple of 2 integer):-")
# print("Patient's Name = ", data_row_img_data[0][((0x0010,0x0010))].value);
# print("Modality = ", data_row_img_data[0][((0x0008, 0x0060))].value);
# print("Body Part Examined = ", data_row_img_data[0][((0x0018, 0x0015))].value);
# print("2. Read By Access Methods:")
# print("Patient's Name = ", data_row_img_data[0].PatientName );
# print("Modality = ", data_row_img_data[0].Modality);
# print("Body Part Examined =", data_row_img_data[0].PatientID) ;
# print("3. Read By Data Frame:")
# print("Patient's Name = ", df_Dicom_train_Image_raw_data.loc[0,"Patient's Name"]);
# print("Body Part Examined = ", df_Dicom_train_Image_raw_data.loc[0,"Modality"]);
# print("Body Part Examined = ", df_Dicom_train_Image_raw_data.loc[0,"Body Part Examined"]);
In [ ]:
#Step 5: Display images with bounding box. [ 5 points ]
sample_patient_ids = list(train_image_class_map.keys())[:5]
image_dir = "/content/drive/MyDrive/Vishal_Notebook/rsna-pneumonia-detection-challenge/stage_2_train_images"
for patient_id in sample_patient_ids:
image_path = os.path.join(image_dir, f"{patient_id}.dcm")
if os.path.exists(image_path):
target = train_image_class_map[patient_id]
preprocess_and_visualize(image_path, target)
else:
print(f"Image not found for patient ID: {patient_id}")
In [ ]:
#view images with target is 1
inspectImages(training_data[training_data['Target']==1].sample(9))
In [ ]:
inspectImages(training_data[training_data['Target']==0].sample(9))
In [ ]:
#Distribution of bounding box counts per patient
plt.figure(figsize=(8, 6))
sns.histplot(training_data['number_of_boxes'], bins=20, kde=True)
plt.title('Distribution of Bounding Box Counts per Patient')
plt.xlabel('Number of Bounding Boxes')
plt.ylabel('Frequency')
plt.show()
#Relationship between target and number of bounding boxes
plt.figure(figsize=(8, 6))
sns.boxplot(x='Target', y='number_of_boxes', data=training_data)
plt.title('Relationship between Target and Number of Bounding Boxes')
plt.xlabel('Target (0: No Pneumonia, 1: Pneumonia)')
plt.ylabel('Number of Bounding Boxes')
plt.show()
#Correlation matrix of numerical features
numerical_features = ['Target', 'x', 'y', 'width', 'height', 'number_of_boxes']
correlation_matrix = training_data[numerical_features].corr()
plt.figure(figsize=(10, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Matrix of Numerical Features')
plt.show()
In [ ]:
#display Metadata of the image so the remaining information can be added to the dataset
sample_patientId = training_data['patientId'][0]
dcm_file = '/content/drive/MyDrive/Vishal_Notebook/rsna-pneumonia-detection-challenge/stage_2_train_images/'+'{}.dcm'.format(sample_patientId)
dcm_data = dcm.dcmread(dcm_file)
print('Metadata of the image consists of \n', dcm_data)
Metadata of the image consists of Dataset.file_meta ------------------------------- (0002,0000) File Meta Information Group Length UL: 202 (0002,0001) File Meta Information Version OB: b'\x00\x01' (0002,0002) Media Storage SOP Class UID UI: Secondary Capture Image Storage (0002,0003) Media Storage SOP Instance UID UI: 1.2.276.0.7230010.3.1.4.8323329.28530.1517874485.775526 (0002,0010) Transfer Syntax UID UI: JPEG Baseline (Process 1) (0002,0012) Implementation Class UID UI: 1.2.276.0.7230010.3.0.3.6.0 (0002,0013) Implementation Version Name SH: 'OFFIS_DCMTK_360' ------------------------------------------------- (0008,0005) Specific Character Set CS: 'ISO_IR 100' (0008,0016) SOP Class UID UI: Secondary Capture Image Storage (0008,0018) SOP Instance UID UI: 1.2.276.0.7230010.3.1.4.8323329.28530.1517874485.775526 (0008,0020) Study Date DA: '19010101' (0008,0030) Study Time TM: '000000.00' (0008,0050) Accession Number SH: '' (0008,0060) Modality CS: 'CR' (0008,0064) Conversion Type CS: 'WSD' (0008,0090) Referring Physician's Name PN: '' (0008,103E) Series Description LO: 'view: PA' (0010,0010) Patient's Name PN: '0004cfab-14fd-4e49-80ba-63a80b6bddd6' (0010,0020) Patient ID LO: '0004cfab-14fd-4e49-80ba-63a80b6bddd6' (0010,0030) Patient's Birth Date DA: '' (0010,0040) Patient's Sex CS: 'F' (0010,1010) Patient's Age AS: '51' (0018,0015) Body Part Examined CS: 'CHEST' (0018,5101) View Position CS: 'PA' (0020,000D) Study Instance UID UI: 1.2.276.0.7230010.3.1.2.8323329.28530.1517874485.775525 (0020,000E) Series Instance UID UI: 1.2.276.0.7230010.3.1.3.8323329.28530.1517874485.775524 (0020,0010) Study ID SH: '' (0020,0011) Series Number IS: '1' (0020,0013) Instance Number IS: '1' (0020,0020) Patient Orientation CS: '' (0028,0002) Samples per Pixel US: 1 (0028,0004) Photometric Interpretation CS: 'MONOCHROME2' (0028,0010) Rows US: 1024 (0028,0011) Columns US: 1024 (0028,0030) Pixel Spacing DS: [0.14300000000000002, 0.14300000000000002] (0028,0100) Bits Allocated US: 8 (0028,0101) Bits Stored US: 8 (0028,0102) High Bit US: 7 (0028,0103) Pixel Representation US: 0 (0028,2110) Lossy Image Compression CS: '01' (0028,2114) Lossy Image Compression Method CS: 'ISO_10918_1' (7FE0,0010) Pixel Data OB: Array of 142006 elements
by looking at above sample we can see that dicom file contains some of the information that can be used for further analysis such as sex, age, body part examined, view position and modality.
Size of this image is 1024 x 1024 (rows x columns).
In [ ]:
from glob import glob
training_image_path = '/content/drive/MyDrive/Vishal_Notebook/rsna-pneumonia-detection-challenge/stage_2_train_images/'
images = pd.DataFrame({'path': glob(os.path.join(training_image_path, '*.dcm'))})
images['patientId'] = images['path'].map(lambda x:os.path.splitext(os.path.basename(x))[0])
print('Columns in the training images dataframe: {}'.format(list(images.columns)))
Columns in the training images dataframe: ['path', 'patientId']
In [ ]:
# Merging the images dataframe with training_data dataframe
training_data = training_data.merge(images, on = 'patientId', how = 'left')
print('After merging the two dataframe, the training_data has {} rows and {} columns.'.format(training_data.shape[0], training_data.shape[1]))
After merging the two dataframe, the training_data has 30227 rows and 9 columns.
In [ ]:
#view final data
print('The training_data dataframe as of now stands like\n')
training_data.head()
The training_data dataframe as of now stands like
Out[ ]:
| patientId | x | y | width | height | Target | class | number_of_boxes | path | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 0004cfab-14fd-4e49-80ba-63a80b6bddd6 | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal | 1 | /content/drive/MyDrive/Vishal_Notebook/rsna-pn... |
| 1 | 00313ee0-9eaa-42f4-b0ab-c148ed3241cd | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal | 1 | /content/drive/MyDrive/Vishal_Notebook/rsna-pn... |
| 2 | 00322d4d-1c29-4943-afc9-b6754be640eb | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal | 1 | /content/drive/MyDrive/Vishal_Notebook/rsna-pn... |
| 3 | 003d8fa0-6bf1-40ed-b54c-ac657f8495c5 | NaN | NaN | NaN | NaN | 0 | Normal | 1 | /content/drive/MyDrive/Vishal_Notebook/rsna-pn... |
| 4 | 00436515-870c-4b36-a041-de91049b9ab4 | 264.0 | 152.0 | 213.0 | 379.0 | 1 | Lung Opacity | 2 | /content/drive/MyDrive/Vishal_Notebook/rsna-pn... |
In [ ]:
# use tqdm
from tqdm import tqdm
# Assuming 'training_data' and 'images' DataFrames are already defined as in your provided code.
metadata_cols = ['Modality', 'PatientAge', 'PatientSex', 'BodyPartExamined', 'ViewPosition', 'ConversionType', 'Rows', 'Columns', 'PixelSpacing']
metadata_df = pd.DataFrame(columns=metadata_cols)
for index, row in tqdm(training_data.iterrows(), total=len(training_data)):
dcm_file = row['path']
try:
dcm_data = dcm.dcmread(dcm_file)
metadata_row = {col: getattr(dcm_data, col, None) for col in metadata_cols} # Handle potential missing attributes
metadata_df = pd.concat([metadata_df, pd.DataFrame([metadata_row])], ignore_index=True)
except Exception as e:
print(f"Error processing {dcm_file}: {e}")
# Handle errors appropriately (e.g., fill with NaN or skip the row)
metadata_row = {col: None for col in metadata_cols} # Fill with NaN for the problematic row
metadata_df = pd.concat([metadata_df, pd.DataFrame([metadata_row])], ignore_index=True)
# Concatenate the metadata with the training data
training_data = pd.concat([training_data, metadata_df], axis=1)
100%|██████████| 30227/30227 [03:26<00:00, 146.14it/s]
In [ ]:
training_data
Out[ ]:
| patientId | x | y | width | height | Target | class | number_of_boxes | path | Modality | PatientAge | PatientSex | BodyPartExamined | ViewPosition | ConversionType | Rows | Columns | PixelSpacing | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0004cfab-14fd-4e49-80ba-63a80b6bddd6 | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal | 1 | /content/drive/MyDrive/Vishal_Notebook/rsna-pn... | CR | 51 | F | CHEST | PA | WSD | 1024 | 1024 | [0.14300000000000002, 0.14300000000000002] |
| 1 | 00313ee0-9eaa-42f4-b0ab-c148ed3241cd | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal | 1 | /content/drive/MyDrive/Vishal_Notebook/rsna-pn... | CR | 48 | F | CHEST | PA | WSD | 1024 | 1024 | [0.19431099999999998, 0.19431099999999998] |
| 2 | 00322d4d-1c29-4943-afc9-b6754be640eb | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal | 1 | /content/drive/MyDrive/Vishal_Notebook/rsna-pn... | CR | 19 | M | CHEST | AP | WSD | 1024 | 1024 | [0.168, 0.168] |
| 3 | 003d8fa0-6bf1-40ed-b54c-ac657f8495c5 | NaN | NaN | NaN | NaN | 0 | Normal | 1 | /content/drive/MyDrive/Vishal_Notebook/rsna-pn... | CR | 28 | M | CHEST | PA | WSD | 1024 | 1024 | [0.14300000000000002, 0.14300000000000002] |
| 4 | 00436515-870c-4b36-a041-de91049b9ab4 | 264.0 | 152.0 | 213.0 | 379.0 | 1 | Lung Opacity | 2 | /content/drive/MyDrive/Vishal_Notebook/rsna-pn... | CR | 32 | F | CHEST | AP | WSD | 1024 | 1024 | [0.139, 0.139] |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 30222 | c1ec14ff-f6d7-4b38-b0cb-fe07041cbdc8 | 185.0 | 298.0 | 228.0 | 379.0 | 1 | Lung Opacity | 2 | /content/drive/MyDrive/Vishal_Notebook/rsna-pn... | CR | 54 | M | CHEST | AP | WSD | 1024 | 1024 | [0.139, 0.139] |
| 30223 | c1edf42b-5958-47ff-a1e7-4f23d99583ba | NaN | NaN | NaN | NaN | 0 | Normal | 1 | /content/drive/MyDrive/Vishal_Notebook/rsna-pn... | CR | 46 | F | CHEST | PA | WSD | 1024 | 1024 | [0.14300000000000002, 0.14300000000000002] |
| 30224 | c1f6b555-2eb1-4231-98f6-50a963976431 | NaN | NaN | NaN | NaN | 0 | Normal | 1 | /content/drive/MyDrive/Vishal_Notebook/rsna-pn... | CR | 45 | M | CHEST | PA | WSD | 1024 | 1024 | [0.168, 0.168] |
| 30225 | c1f7889a-9ea9-4acb-b64c-b737c929599a | 570.0 | 393.0 | 261.0 | 345.0 | 1 | Lung Opacity | 2 | /content/drive/MyDrive/Vishal_Notebook/rsna-pn... | CR | 72 | F | CHEST | AP | WSD | 1024 | 1024 | [0.139, 0.139] |
| 30226 | c1f7889a-9ea9-4acb-b64c-b737c929599a | 233.0 | 424.0 | 201.0 | 356.0 | 1 | Lung Opacity | 2 | /content/drive/MyDrive/Vishal_Notebook/rsna-pn... | CR | 72 | F | CHEST | AP | WSD | 1024 | 1024 | [0.139, 0.139] |
30227 rows × 18 columns
In [ ]:
# Saving the training_data for further use:
training_data.to_pickle('training_data.pkl')
In [ ]:
print('Modality for the images obtained is: {} \n'.format(training_data['Modality'].unique()[0]))
Modality for the images obtained is: CR
In [ ]:
#check and examine other added columns with differnt analysis
training_data.columns
Out[ ]:
Index(['patientId', 'x', 'y', 'width', 'height', 'Target', 'class',
'number_of_boxes', 'path', 'Modality', 'PatientAge', 'PatientSex',
'BodyPartExamined', 'ViewPosition', 'ConversionType', 'Rows', 'Columns',
'PixelSpacing'],
dtype='object')
In [ ]:
get_feature_distribution(training_data.drop_duplicates('patientId'), 'PatientSex')
Feature: PatientSex M : 15166 which is 56.83% of the total data in the dataset F : 11518 which is 43.16% of the total data in the dataset
In [ ]:
#Patient their sex wise class distribution
plt.figure(figsize=(8, 6))
sns.countplot(x='PatientSex', hue='class', data=training_data.drop_duplicates('patientId'))
plt.title('Patient Sex vs. Class Distribution')
plt.xlabel('Patient Sex')
plt.ylabel('Count')
plt.show()
In [ ]:
#patient their sex wise target distribution
plt.figure(figsize=(8, 6))
sns.countplot(x='PatientSex', hue='Target', data=training_data.drop_duplicates('patientId'))
plt.title('Patient Sex vs. Target Distribution')
plt.xlabel('Patient Sex')
plt.ylabel('Count')
plt.show()
In [ ]:
print('Distribution of `PatientAge`: Overall and Target = 1')
fig = plt.figure(figsize = (10, 6))
ax = fig.add_subplot(121)
g = (sns.distplot(training_data['PatientAge']).set_title('Distribution of PatientAge'))
ax = fig.add_subplot(122)
g = (sns.distplot(training_data.drop_duplicates('patientId').loc[training_data['Target'] == 1, 'PatientAge']).set_title('Distribution of PatientAge vs PnuemoniaEvidence'))
Distribution of `PatientAge`: Overall and Target = 1
<ipython-input-62-4a4a96525421>:5: UserWarning:
`distplot` is a deprecated function and will be removed in seaborn v0.14.0.
Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).
For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751
g = (sns.distplot(training_data['PatientAge']).set_title('Distribution of PatientAge'))
<ipython-input-62-4a4a96525421>:8: UserWarning:
`distplot` is a deprecated function and will be removed in seaborn v0.14.0.
Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).
For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751
g = (sns.distplot(training_data.drop_duplicates('patientId').loc[training_data['Target'] == 1, 'PatientAge']).set_title('Distribution of PatientAge vs PnuemoniaEvidence'))
In [ ]:
print('Check images obtained near {} areas.'.format(training_data['BodyPartExamined'].unique()[0]))
Check images obtained near CHEST areas.
In [ ]:
get_feature_distribution(training_data.drop_duplicates('patientId'), 'ViewPosition')
Feature: ViewPosition PA : 14511 which is 54.38% of the total data in the dataset AP : 12173 which is 45.61% of the total data in the dataset
The ViewPosition attribute in the DICOM metadata describes the patient's position during the X-ray acquisition. Common values include:
- PA (Posteroanterior): X-ray beam enters the posterior (back) of the patient and exits the anterior (front). This is the standard view for chest X-rays.
- AP (Anteroposterior): X-ray beam enters the anterior (front) and exits the posterior (back). Often used for patients who can't stand or when a portable X-ray machine is used.
Image Quality Assessment: Different view positions might affect image quality and the visibility of certain lung regions. Analyzing the distribution can reveal if certain views are under- or over-represented, potentially impacting model performance.
In summary, ViewPosition is a crucial piece of metadata that provides context about how the image was acquired and can potentially improve the analysis or modeling of the chest X-rays.
In [ ]:
#Check AP and PA distribution where target=1 (phenumonia)
print('The distribution of View Position when there is an evidence of Pneumonia:\n')
_ = training_data.drop_duplicates('patientId').loc[training_data['Target'] == 1, 'ViewPosition'].value_counts().sort_index(ascending = False).plot(kind = 'pie', autopct = '%.0f%%').set_ylabel('')
The distribution of View Position when there is an evidence of Pneumonia:
In [ ]:
print('Conversion Type for the data in Training Data: ', training_data['ConversionType'].unique()[0])
Conversion Type for the data in Training Data: WSD
In [ ]:
print(f'The training images has {training_data.Rows.unique()[0]} rows and {training_data.Columns.unique()[0]} columns.')
The training images has 1024 rows and 1024 columns.
In [ ]:
print('The minimum and maximum recorded age of the patients are {} and {} respectively.'.format(training_data['PatientAge'].min(), training_data['PatientAge'].max()))
The minimum and maximum recorded age of the patients are 1 and 92 respectively.
In [ ]:
# Convert 'PatientAge' to numeric, handling errors
training_data['PatientAge'] = pd.to_numeric(training_data['PatientAge'], errors='coerce')
plt.figure(figsize=(10, 6))
sns.histplot(training_data['PatientAge'], kde=True)
plt.title('Distribution of Patient Age')
plt.xlabel('Patient Age')
plt.ylabel('Frequency')
plt.show()
plt.figure(figsize=(10, 6))
sns.histplot(training_data[training_data['Target'] == 1]['PatientAge'], kde=True)
plt.title('Distribution of Patient Age for Patients with Pneumonia')
plt.xlabel('Patient Age')
plt.ylabel('Frequency')
plt.show()
# Calculate patient percentages by age group
age_groups = pd.cut(training_data['PatientAge'], bins=range(0, 101, 10))
age_percentages = training_data.groupby(age_groups)['patientId'].count() / len(training_data) * 100
#added agewise bins to dataset
training_data['PatientAgeBins'] = pd.cut(training_data['PatientAge'], bins=range(0, 101, 10))
training_data.drop_duplicates('patientId')['PatientAgeBins'].value_counts()
# Plot the patient percentages by age group
plt.figure(figsize=(12, 6))
age_percentages.plot(kind='bar')
plt.title('Patient Percentage by Age Group')
plt.xlabel('Age Group')
plt.ylabel('Percentage of Patients')
plt.xticks(rotation=45)
plt.show()
<ipython-input-69-367fd3f1a3ba>:21: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. age_percentages = training_data.groupby(age_groups)['patientId'].count() / len(training_data) * 100
In [ ]:
training_data.head()
Out[ ]:
| patientId | x | y | width | height | Target | class | number_of_boxes | path | Modality | PatientAge | PatientSex | BodyPartExamined | ViewPosition | ConversionType | Rows | Columns | PixelSpacing | PatientAgeBins | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0004cfab-14fd-4e49-80ba-63a80b6bddd6 | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal | 1 | /content/drive/MyDrive/Vishal_Notebook/rsna-pn... | CR | 51 | F | CHEST | PA | WSD | 1024 | 1024 | [0.14300000000000002, 0.14300000000000002] | (50, 60] |
| 1 | 00313ee0-9eaa-42f4-b0ab-c148ed3241cd | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal | 1 | /content/drive/MyDrive/Vishal_Notebook/rsna-pn... | CR | 48 | F | CHEST | PA | WSD | 1024 | 1024 | [0.19431099999999998, 0.19431099999999998] | (40, 50] |
| 2 | 00322d4d-1c29-4943-afc9-b6754be640eb | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal | 1 | /content/drive/MyDrive/Vishal_Notebook/rsna-pn... | CR | 19 | M | CHEST | AP | WSD | 1024 | 1024 | [0.168, 0.168] | (10, 20] |
| 3 | 003d8fa0-6bf1-40ed-b54c-ac657f8495c5 | NaN | NaN | NaN | NaN | 0 | Normal | 1 | /content/drive/MyDrive/Vishal_Notebook/rsna-pn... | CR | 28 | M | CHEST | PA | WSD | 1024 | 1024 | [0.14300000000000002, 0.14300000000000002] | (20, 30] |
| 4 | 00436515-870c-4b36-a041-de91049b9ab4 | 264.0 | 152.0 | 213.0 | 379.0 | 1 | Lung Opacity | 2 | /content/drive/MyDrive/Vishal_Notebook/rsna-pn... | CR | 32 | F | CHEST | AP | WSD | 1024 | 1024 | [0.139, 0.139] | (30, 40] |
In [ ]:
# Plot patient age bins vs patient sex
plt.figure(figsize=(12, 6))
sns.countplot(x='PatientAgeBins', hue='PatientSex', data=training_data.drop_duplicates('patientId'))
plt.title('Patient Age Bins vs. Patient Sex Distribution')
plt.xlabel('Patient Age Bins')
plt.ylabel('Count')
plt.xticks(rotation=45)
plt.show()
In [ ]:
# Plot patient age bins, patient sex, and class
plt.figure(figsize=(12, 6))
sns.countplot(x='PatientAgeBins', hue='class', data=training_data.drop_duplicates('patientId'))
plt.title('Patient Age Bins vs. Class Distribution')
plt.xlabel('Patient Age Bins')
plt.ylabel('Count')
plt.xticks(rotation=45)
plt.show()
1. Class Imbalance:
The pie chart shows a significant class imbalance, with a majority of patients being negative for pneumonia. This imbalance needs to be addressed during model training (e.g., using class weights or resampling techniques).
2. Bounding Box Analysis:
- The code shows that most patients have a single bounding box.
- A small percentage have multiple bounding boxes, indicating multiple pneumonia instances in a single image.
- The distribution plot of bounding boxes shows the frequency of patient's with different numbers of boxes.
- The box plot comparing the target variable and the number of bounding boxes helps visualize the relationship between the presence of pneumonia and the number of bounding boxes detected.
3. Image Metadata Analysis: The code extracts various metadata fields (Modality, PatientAge, PatientSex, etc.) from DICOM files.
The analysis can be improved using this metadata:
- Patient Sex: Analyzing the relationship between patient sex and the presence or absence of pneumonia.
- Patient Age: Investigating the distribution of patient ages, identifying if certain age groups have a higher prevalence of pneumonia.
- The distributions of ages of all patients and patients with pneumonia are shown in separate histograms.
- Age bins were created, and counts of patients within those age bins were displayed.
- A bar chart displays the percentage of patients within each age group.
- Body Part Examined: Checking if all images are of the chest area.
- View Position: Analyzing the distribution of 'PA' and 'AP' views, potentially assessing if certain view positions might bias the model.
- A pie chart shows the distribution of view position for patients with pneumonia.
- Pixel Spacing and Image Size: Analyzing if there are inconsistencies in image dimensions or pixel spacing that could affect model performance.
- The code prints the row and column size of the images.
- Correlation Matrix: The correlation heatmap reveals relationships between numerical features, including 'Target', bounding box coordinates, and the number of bounding boxes.
4. Data Visualization:
- The code visualizes the sample images with bounding boxes overlaid.
- The visualizations are crucial for understanding how the model is performing.
Summary
- class imbalance is present in the dataset.
- The training dataset(both of the csv files and training image folder) contains information of 26684 patient (unique)
- Most of the recorded patient don't have pneumonia(target-0)
- some of the patients have more than one bounding box. The maximum being 4.
- The classes "No Lung Opacity/Not Normal" and "Normal" is associated with the target = 0 whereas "Lung Opacity" belong to Target = 1.
- The images are present in dicom format, from which information like patientAge, PatientSex, ViewPosition etc are obtained
- There are two ways from which images were ontained AP and PA. The age ranges from 1- 92.
Model Building
In [ ]:
#Step 6: Design, train and test basic CNN models for classification.
In [ ]:
#import libraries
import numpy as np
import pickle
import matplotlib.pyplot as plt
import cv2
import os
import tensorflow as tf
from tensorflow.keras.layers import ( Input,
Conv2D,
MaxPooling2D,
UpSampling2D,
Concatenate,
Activation,
Dropout,BatchNormalization,
Reshape,ZeroPadding2D, Convolution2D, Flatten,Dense
)
import functools
from tensorflow import keras
from tensorflow.keras.models import Model,Sequential
from tensorflow.keras.applications import MobileNet #mobilenet architecture.
from tensorflow.keras.applications.mobilenet import preprocess_input
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau
from tensorflow.keras.metrics import Recall, Precision
from tensorflow.keras import backend as K
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
import matplotlib.pyplot as plt
import pandas as pd
import random
from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Input
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.utils import Sequence
import random
In [ ]:
## take a 1000 images from the dataset for processing
model_trainigdata = training_data.groupby('class', group_keys=False).apply(lambda x: x.sample(10))
<ipython-input-75-11af65491338>:2: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
model_trainigdata = training_data.groupby('class', group_keys=False).apply(lambda x: x.sample(10))
In [ ]:
model_trainigdata["class"].value_counts()
Out[ ]:
| count | |
|---|---|
| class | |
| Lung Opacity | 10 |
| No Lung Opacity / Not Normal | 10 |
| Normal | 10 |
In [ ]:
model_trainigdata.info()
<class 'pandas.core.frame.DataFrame'> Index: 30 entries, 17570 to 17064 Data columns (total 19 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 patientId 30 non-null object 1 x 10 non-null float64 2 y 10 non-null float64 3 width 10 non-null float64 4 height 10 non-null float64 5 Target 30 non-null int64 6 class 30 non-null object 7 number_of_boxes 30 non-null int64 8 path 30 non-null object 9 Modality 30 non-null object 10 PatientAge 30 non-null int64 11 PatientSex 30 non-null object 12 BodyPartExamined 30 non-null object 13 ViewPosition 30 non-null object 14 ConversionType 30 non-null object 15 Rows 30 non-null object 16 Columns 30 non-null object 17 PixelSpacing 30 non-null object 18 PatientAgeBins 30 non-null category dtypes: category(1), float64(4), int64(3), object(11) memory usage: 4.9+ KB
In [ ]:
train_df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 30227 entries, 0 to 30226 Data columns (total 6 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 patientId 30227 non-null object 1 x 9555 non-null float64 2 y 9555 non-null float64 3 width 9555 non-null float64 4 height 9555 non-null float64 5 Target 30227 non-null int64 dtypes: float64(4), int64(1), object(1) memory usage: 1.4+ MB
In [ ]:
images = []
ADJUSTED_IMAGE_SIZE = 128
imageList = []
classLabels = []
labels = []
originalImage = []
# Function to read the image from the path and reshape the image to size
def readAndReshapeImage(image):
img = np.array(image).astype(np.uint8)
## Resize the image
res = cv2.resize(img,(ADJUSTED_IMAGE_SIZE,ADJUSTED_IMAGE_SIZE), interpolation = cv2.INTER_LINEAR)
return res
## Read the imahge and resize the image
def populateImage(rowData):
for index, row in rowData.iterrows():
patientId = row.patientId
classlabel = row["class"]
dcm_file = '/content/drive/MyDrive/Vishal_Notebook/rsna-pneumonia-detection-challenge/stage_2_train_images/'+'{}.dcm'.format(patientId)
dcm_data = dcm.dcmread(dcm_file)
img = dcm_data.pixel_array
## Converting the image to 3 channels as the dicom image pixel does not have colour classes wiht it
if len(img.shape) != 3 or img.shape[2] != 3:
img = np.stack((img,) * 3, -1)
imageList.append(readAndReshapeImage(img))
# originalImage.append(img)
classLabels.append(classlabel)
tmpImages = np.array(imageList)
tmpLabels = np.array(classLabels)
# originalImages = np.array(originalImage)
return tmpImages,tmpLabels
# Function to create a dataframe for results
def createResultDf(name,accuracy,testscore):
result = pd.DataFrame({'Method':[name], 'accuracy': [accuracy] ,'Test Score':[testscore]})
return result
In [ ]:
## Reading the images into numpy array
images,labels = populateImage(model_trainigdata)
In [ ]:
#encoding the lablels
enc = LabelBinarizer()
y2 = enc.fit_transform(labels)
In [ ]:
# splitting into train ,test and validation data
X_train, X_test, y_train, y_test = train_test_split(images, y2, test_size=0.3, random_state=50)
X_test, X_val, y_test, y_val = train_test_split(X_test,y_test, test_size = 0.5, random_state=50)
In [ ]:
#Create basic vgg16 cnn model for classification.
def create_vgg16_model(input_shape, num_classes):
model = Sequential()
# 32 filters with 3x3 kernal and no padding
model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=input_shape))
model.add(Conv2D(32, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
# 64 filters with 3x3 kernal and no padding
model.add(Conv2D(64, (3, 3), activation='relu', padding='same', input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
# # 128 filters with 3x3 kernal and no padding
model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))
model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
# # # 256 filters with 3x3 kernal and no padding
# model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))
# model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))
# model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))
# model.add(MaxPooling2D((2, 2), strides=(2, 2)))
# Classification block
model.add(Flatten())
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
return model
In [ ]:
# Model summary
input_shape = (128, 128, 3) # Adjust input shape as needed
num_classes = 3 # Replace with the actual number of classes
vgg16_model = create_vgg16_model(input_shape, num_classes)
vgg16_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
vgg16_model.summary()
/usr/local/lib/python3.10/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ conv2d (Conv2D) │ (None, 128, 128, 32) │ 896 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ conv2d_1 (Conv2D) │ (None, 128, 128, 32) │ 9,248 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ max_pooling2d (MaxPooling2D) │ (None, 64, 64, 32) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ conv2d_2 (Conv2D) │ (None, 64, 64, 64) │ 18,496 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ conv2d_3 (Conv2D) │ (None, 64, 64, 64) │ 36,928 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ max_pooling2d_1 (MaxPooling2D) │ (None, 32, 32, 64) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ conv2d_4 (Conv2D) │ (None, 32, 32, 128) │ 73,856 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ conv2d_5 (Conv2D) │ (None, 32, 32, 128) │ 147,584 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ max_pooling2d_2 (MaxPooling2D) │ (None, 16, 16, 128) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ flatten_1 (Flatten) │ (None, 32768) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_1 (Dense) │ (None, 4096) │ 134,221,824 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_1 (Dropout) │ (None, 4096) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_2 (Dense) │ (None, 4096) │ 16,781,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_2 (Dropout) │ (None, 4096) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_3 (Dense) │ (None, 3) │ 12,291 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 151,302,435 (577.17 MB)
Trainable params: 151,302,435 (577.17 MB)
Non-trainable params: 0 (0.00 B)
In [ ]:
# Now Train the model
epochs = 2 # Adjust the number of epochs as needed
batch_size = 32 # Adjust the batch size as needed
history = vgg16_model.fit(
X_train, y_train,
epochs=epochs,
batch_size=batch_size,
validation_data=(X_val, y_val)
)
# Evaluate the model
loss, accuracy = vgg16_model.evaluate(X_test, y_test, verbose=0)
print(f"Test Loss: {loss:.4f}")
print(f"Test Accuracy: {accuracy:.4f}")
#Further analysis and model improvement can be done here. Example below
#Plot training & validation accuracy values
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
# Plot training & validation loss values
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
Epoch 1/2 1/1 ━━━━━━━━━━━━━━━━━━━━ 10s 10s/step - accuracy: 0.2857 - loss: 13.1125 - val_accuracy: 0.2000 - val_loss: 64.8382 Epoch 2/2 1/1 ━━━━━━━━━━━━━━━━━━━━ 6s 6s/step - accuracy: 0.6190 - loss: 68.7941 - val_accuracy: 0.6000 - val_loss: 382.2352 Test Loss: 855.0109 Test Accuracy: 0.2500
In [ ]:
#Further analysis and model improvement can be done here. Example below
#Plot training & validation accuracy values
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
# Plot training & validation loss values
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
# Example: Confusion Matrix
from sklearn.metrics import confusion_matrix
import seaborn as sns
y_pred = vgg16_model.predict(X_test)
y_pred_classes = np.argmax(y_pred, axis=1)
y_true_classes = np.argmax(y_test, axis=1)
conf_matrix = confusion_matrix(y_true_classes, y_pred_classes)
plt.figure(figsize=(8, 6))
sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')
plt.title('Confusion Matrix')
plt.xlabel('Predicted Labels')
plt.ylabel('True Labels')
plt.show()
# Example: Try different optimizers, learning rates, or model architectures
# Example: Add regularization (dropout, L1/L2) to prevent overfitting
# Example: Data augmentation to increase training data diversity
# Example of saving the trained model
vgg16_model.save("vgg16_pneumonia_model.h5")
# Example of loading the saved model:
# from tensorflow.keras.models import load_model
# loaded_model = load_model("vgg16_pneumonia_model.h5")
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 403ms/step
WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`.
In [ ]:
Analysis of the provided code:
- Data Loading and Preprocessing:
The code loads data from DICOM files, merges it with metadata, and performs exploratory data analysis (EDA).
Key preprocessing steps include: - Merging dataframes: Combines image metadata and patient information. - DICOM metadata extraction: Reads relevant metadata fields (Modality, PatientAge, PatientSex, etc.). Handles missing values. - Data visualization: Generates various plots (distributions, histograms, count plots) to understand data characteristics.
Exploratory Data Analysis (EDA):
- The EDA section provides valuable insights into the dataset, including:
- Class imbalance: Visualizes class distribution, revealing a significant imbalance between classes.
- Patient demographics: Analyzes age, sex distributions, and their relationships to the target variable.
- Image metadata: Examines various DICOM metadata like ViewPosition, BodyPartExamined, ConversionType, image size, etc.
- Visualizations like histograms, countplots, pie charts, and bar charts are employed for comprehensive data understanding.
- The EDA section provides valuable insights into the dataset, including:
Model Building (VGG16-like CNN):
- A custom CNN model, inspired by VGG16, is created using Keras.
- Architecture: The model consists of convolutional layers, max-pooling layers, flatten layer, dense layers, and a softmax output layer.
- Compilation: The model is compiled with the Adam optimizer, categorical cross-entropy loss, and accuracy as the metric.
- Training: The model is trained for 30 epochs with a batch size of 32. Validation data is used for monitoring performance during training.
- Evaluation: The trained model is evaluated on a separate test set using test loss and accuracy as metrics.
Model Evaluation and Visualization:
- Performance metrics: Presents test loss and accuracy.
- Visualization: Displays training/validation accuracy and loss curves over epochs, providing insights into model learning trends.
- Confusion matrix: Visualizes the model's predictions against true labels, showing performance for each class.
Model Saving and Loading:
- The trained model is saved to a file (vgg16_pneumonia_model.h5) for future use.
- Code for loading the model is provided for convenience.
Areas for Improvement:
- Handling class imbalance: Strategies like oversampling, undersampling, or using class weights during training could improve performance.
- Data augmentation: Applying random transformations to the training images can help the model generalize better and potentially reduce overfitting.
- Hyperparameter tuning: Experiment with different optimizers, learning rates, batch sizes, and network architectures to optimize model performance.
- Feature engineering: Extract additional features from DICOM files or images that may improve classification accuracy.
- Model regularization: Add dropout or L1/L2 regularization to the model to mitigate potential overfitting.
- More detailed error analysis: Examine misclassified images to understand why they are difficult for the model.
- Experiment with different CNN architectures: Explore more advanced models or transfer learning techniques using pre-trained models.
6.2 Import a Pretrained Model of MobileNetV2¶
In [ ]:
# Set target size (e.g., 224x224 for MobileNetV2)
IMG_SIZE = (224, 224)
# Load pretrained model without the top layers
base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(IMG_SIZE[0], IMG_SIZE[1], 3))
# Add custom layers for pneumonia classification
x = base_model.output
x = GlobalAveragePooling2D()(x) # Global average pooling
x = Dense(128, activation='relu')(x) # Fully connected layer
predictions = Dense(1, activation='sigmoid')(x) # Sigmoid for binary classification
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5 9406464/9406464 ━━━━━━━━━━━━━━━━━━━━ 0s 0us/step
In [ ]:
#Combine base model with custom layers
model = Model(inputs=base_model.input, outputs=predictions)
# Freeze base model layers for transfer learning
for layer in base_model.layers:
layer.trainable = False
# Compile the model
model.compile(optimizer=Adam(learning_rate=0.001), loss='binary_crossentropy', metrics=['accuracy'])
In [ ]:
import pydicom as dcm
def populateImage_MobileNet(dicom_path, target_size):
dicom = dcm.dcmread(dicom_path) # Load DICOM file
img = dicom.pixel_array # Extract pixel array
if img.dtype == np.float64:
img = (img * 255 / img.max()).astype(np.uint8) # Normalize
img_resized = cv2.resize(img, target_size) # Resize to target size
if len(img_resized.shape) == 2: # If single-channel
img_resized = cv2.cvtColor(img_resized, cv2.COLOR_GRAY2RGB) # Convert single channel (grayscale) to 3 channels
return img_resized
In [ ]:
class DICOMDataGenerator(Sequence):
#This method initializes the generator with required data and settings.
def __init__(self, file_paths, labels, batch_size, target_size):
self.file_paths = file_paths
self.labels = labels
self.batch_size = batch_size
self.target_size = target_size
#Returns the number of batches per epoch.
def __len__(self):
return int(np.ceil(len(self.file_paths) / self.batch_size))
# Fetching a Batch - Loads and preprocesses a batch of DICOM images and their corresponding labels.
def __getitem__(self, index):
batch_paths = self.file_paths[index * self.batch_size:(index + 1) * self.batch_size]
batch_labels = self.labels[index * self.batch_size:(index + 1) * self.batch_size]
images = []
for path in batch_paths:
img = populateImage_MobileNet(path, self.target_size)
images.append(img)
return np.array(images), np.array(batch_labels)
#Shuffles the data at the end of each epoch to improve training randomness.
def on_epoch_end(self):
# Shuffle the data at the end of each epoch
combined = list(zip(self.file_paths, self.labels))
random.shuffle(combined)
self.file_paths, self.labels = zip(*combined)
In [ ]:
# Example: List of DICOM file paths and labels (0 for normal, 1 for pneumonia)
dicom_paths = model_trainigdata.head(100)['path'].tolist();
labels = model_trainigdata.head(100)['Target'].tolist();
# Split dataset
train_paths, val_paths, train_labels, val_labels = train_test_split(dicom_paths, labels, test_size=0.2, random_state=42)
# Create data generators
batch_size = 10
train_gen = DICOMDataGenerator(train_paths, train_labels, batch_size, IMG_SIZE)
val_gen = DICOMDataGenerator(val_paths, val_labels, batch_size, IMG_SIZE)
In [ ]:
# Train the model
history = model.fit(
train_gen,
validation_data=val_gen,
epochs=10,
steps_per_epoch=len(train_gen),
validation_steps=len(val_gen)
)
Epoch 1/10
/usr/local/lib/python3.10/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:122: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored. self._warn_if_super_not_called()
3/3 ━━━━━━━━━━━━━━━━━━━━ 8s 1s/step - accuracy: 0.4321 - loss: 0.8431 - val_accuracy: 0.6667 - val_loss: 0.8931 Epoch 2/10
/usr/lib/python3.10/contextlib.py:153: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset. self.gen.throw(typ, value, traceback)
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 112ms/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 Epoch 3/10 3/3 ━━━━━━━━━━━━━━━━━━━━ 2s 637ms/step - accuracy: 0.7756 - loss: 0.3856 - val_accuracy: 0.3333 - val_loss: 0.8077 Epoch 4/10 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 65ms/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 Epoch 5/10 3/3 ━━━━━━━━━━━━━━━━━━━━ 2s 567ms/step - accuracy: 0.5583 - loss: 0.5193 - val_accuracy: 0.6667 - val_loss: 0.9280 Epoch 6/10 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 Epoch 7/10 3/3 ━━━━━━━━━━━━━━━━━━━━ 1s 466ms/step - accuracy: 0.7173 - loss: 0.3719 - val_accuracy: 0.6667 - val_loss: 1.2829 Epoch 8/10 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 Epoch 9/10 3/3 ━━━━━━━━━━━━━━━━━━━━ 1s 364ms/step - accuracy: 0.7702 - loss: 0.3367 - val_accuracy: 0.6667 - val_loss: 0.9733 Epoch 10/10 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.0000e+00 - loss: 0.0000e+00
In [ ]:
test_loss, test_accuracy = model.evaluate(val_gen)
print(f"Test Loss: {test_loss}, Test Accuracy: {test_accuracy}")
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 249ms/step - accuracy: 0.6667 - loss: 0.9733 Test Loss: 0.9733180403709412, Test Accuracy: 0.6666666865348816
In [ ]:
for layer in base_model.layers:
layer.trainable = True
# Compile with a lower learning rate for fine-tuning
model.compile(optimizer=Adam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy'])
# Continue training
model.fit(
train_gen,
validation_data=val_gen,
epochs=5,
steps_per_epoch=len(train_gen),
validation_steps=len(val_gen)
)
Epoch 1/5 3/3 ━━━━━━━━━━━━━━━━━━━━ 39s 3s/step - accuracy: 0.7798 - loss: 0.7662 - val_accuracy: 0.6667 - val_loss: 1.1181 Epoch 2/5 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 55ms/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 Epoch 3/5 3/3 ━━━━━━━━━━━━━━━━━━━━ 7s 1s/step - accuracy: 0.7500 - loss: 0.3147 - val_accuracy: 0.6667 - val_loss: 0.9934 Epoch 4/5 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 Epoch 5/5 3/3 ━━━━━━━━━━━━━━━━━━━━ 5s 2s/step - accuracy: 1.0000 - loss: 0.0991 - val_accuracy: 0.6667 - val_loss: 0.9556
Out[ ]:
<keras.src.callbacks.history.History at 0x7c490120abf0>
In [ ]:
# ResNet clasification model
from keras.applications import ResNet50
from keras.models import Model
from keras.layers import Dense, Flatten, Dropout
from keras.optimizers import Adam
# Load the ResNet50 model without the top layers
base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(128, 128, 3))
# Freeze the base model layers (optional, if you use pre-trained weights)
base_model.trainable = False
# Add custom classification layers on top
x = base_model.output
x = Flatten()(x) # Flatten the feature maps
x = Dense(1024, activation='relu')(x)
x = Dropout(0.5)(x) # Dropout for regularization
x = Dense(num_classes, activation='softmax')(x) # Replace with the number of classes in your dataset
# Create the full model
resnet_model = Model(inputs=base_model.input, outputs=x)
# Compile the model
resnet_model.compile(
optimizer=Adam(learning_rate=0.001),
loss='categorical_crossentropy',
metrics=['accuracy']
)
# Model summary
resnet_model.summary()
Model: "functional_16"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ Connected to ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ │ input_layer_3 │ (None, 128, 128, 3) │ 0 │ - │ │ (InputLayer) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv1_pad (ZeroPadding2D) │ (None, 134, 134, 3) │ 0 │ input_layer_3[0][0] │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv1_conv (Conv2D) │ (None, 64, 64, 64) │ 9,472 │ conv1_pad[0][0] │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv1_bn │ (None, 64, 64, 64) │ 256 │ conv1_conv[0][0] │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv1_relu (Activation) │ (None, 64, 64, 64) │ 0 │ conv1_bn[0][0] │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ pool1_pad (ZeroPadding2D) │ (None, 66, 66, 64) │ 0 │ conv1_relu[0][0] │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ pool1_pool (MaxPooling2D) │ (None, 32, 32, 64) │ 0 │ pool1_pad[0][0] │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv2_block1_1_conv │ (None, 32, 32, 64) │ 4,160 │ pool1_pool[0][0] │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv2_block1_1_bn │ (None, 32, 32, 64) │ 256 │ conv2_block1_1_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv2_block1_1_relu │ (None, 32, 32, 64) │ 0 │ conv2_block1_1_bn[0][… │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv2_block1_2_conv │ (None, 32, 32, 64) │ 36,928 │ conv2_block1_1_relu[0… │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv2_block1_2_bn │ (None, 32, 32, 64) │ 256 │ conv2_block1_2_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv2_block1_2_relu │ (None, 32, 32, 64) │ 0 │ conv2_block1_2_bn[0][… │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv2_block1_0_conv │ (None, 32, 32, 256) │ 16,640 │ pool1_pool[0][0] │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv2_block1_3_conv │ (None, 32, 32, 256) │ 16,640 │ conv2_block1_2_relu[0… │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv2_block1_0_bn │ (None, 32, 32, 256) │ 1,024 │ conv2_block1_0_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv2_block1_3_bn │ (None, 32, 32, 256) │ 1,024 │ conv2_block1_3_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv2_block1_add (Add) │ (None, 32, 32, 256) │ 0 │ conv2_block1_0_bn[0][… │ │ │ │ │ conv2_block1_3_bn[0][… │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv2_block1_out │ (None, 32, 32, 256) │ 0 │ conv2_block1_add[0][0] │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv2_block2_1_conv │ (None, 32, 32, 64) │ 16,448 │ conv2_block1_out[0][0] │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv2_block2_1_bn │ (None, 32, 32, 64) │ 256 │ conv2_block2_1_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv2_block2_1_relu │ (None, 32, 32, 64) │ 0 │ conv2_block2_1_bn[0][… │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv2_block2_2_conv │ (None, 32, 32, 64) │ 36,928 │ conv2_block2_1_relu[0… │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv2_block2_2_bn │ (None, 32, 32, 64) │ 256 │ conv2_block2_2_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv2_block2_2_relu │ (None, 32, 32, 64) │ 0 │ conv2_block2_2_bn[0][… │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv2_block2_3_conv │ (None, 32, 32, 256) │ 16,640 │ conv2_block2_2_relu[0… │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv2_block2_3_bn │ (None, 32, 32, 256) │ 1,024 │ conv2_block2_3_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv2_block2_add (Add) │ (None, 32, 32, 256) │ 0 │ conv2_block1_out[0][0… │ │ │ │ │ conv2_block2_3_bn[0][… │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv2_block2_out │ (None, 32, 32, 256) │ 0 │ conv2_block2_add[0][0] │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv2_block3_1_conv │ (None, 32, 32, 64) │ 16,448 │ conv2_block2_out[0][0] │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv2_block3_1_bn │ (None, 32, 32, 64) │ 256 │ conv2_block3_1_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv2_block3_1_relu │ (None, 32, 32, 64) │ 0 │ conv2_block3_1_bn[0][… │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv2_block3_2_conv │ (None, 32, 32, 64) │ 36,928 │ conv2_block3_1_relu[0… │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv2_block3_2_bn │ (None, 32, 32, 64) │ 256 │ conv2_block3_2_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv2_block3_2_relu │ (None, 32, 32, 64) │ 0 │ conv2_block3_2_bn[0][… │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv2_block3_3_conv │ (None, 32, 32, 256) │ 16,640 │ conv2_block3_2_relu[0… │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv2_block3_3_bn │ (None, 32, 32, 256) │ 1,024 │ conv2_block3_3_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv2_block3_add (Add) │ (None, 32, 32, 256) │ 0 │ conv2_block2_out[0][0… │ │ │ │ │ conv2_block3_3_bn[0][… │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv2_block3_out │ (None, 32, 32, 256) │ 0 │ conv2_block3_add[0][0] │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block1_1_conv │ (None, 16, 16, 128) │ 32,896 │ conv2_block3_out[0][0] │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block1_1_bn │ (None, 16, 16, 128) │ 512 │ conv3_block1_1_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block1_1_relu │ (None, 16, 16, 128) │ 0 │ conv3_block1_1_bn[0][… │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block1_2_conv │ (None, 16, 16, 128) │ 147,584 │ conv3_block1_1_relu[0… │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block1_2_bn │ (None, 16, 16, 128) │ 512 │ conv3_block1_2_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block1_2_relu │ (None, 16, 16, 128) │ 0 │ conv3_block1_2_bn[0][… │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block1_0_conv │ (None, 16, 16, 512) │ 131,584 │ conv2_block3_out[0][0] │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block1_3_conv │ (None, 16, 16, 512) │ 66,048 │ conv3_block1_2_relu[0… │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block1_0_bn │ (None, 16, 16, 512) │ 2,048 │ conv3_block1_0_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block1_3_bn │ (None, 16, 16, 512) │ 2,048 │ conv3_block1_3_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block1_add (Add) │ (None, 16, 16, 512) │ 0 │ conv3_block1_0_bn[0][… │ │ │ │ │ conv3_block1_3_bn[0][… │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block1_out │ (None, 16, 16, 512) │ 0 │ conv3_block1_add[0][0] │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block2_1_conv │ (None, 16, 16, 128) │ 65,664 │ conv3_block1_out[0][0] │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block2_1_bn │ (None, 16, 16, 128) │ 512 │ conv3_block2_1_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block2_1_relu │ (None, 16, 16, 128) │ 0 │ conv3_block2_1_bn[0][… │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block2_2_conv │ (None, 16, 16, 128) │ 147,584 │ conv3_block2_1_relu[0… │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block2_2_bn │ (None, 16, 16, 128) │ 512 │ conv3_block2_2_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block2_2_relu │ (None, 16, 16, 128) │ 0 │ conv3_block2_2_bn[0][… │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block2_3_conv │ (None, 16, 16, 512) │ 66,048 │ conv3_block2_2_relu[0… │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block2_3_bn │ (None, 16, 16, 512) │ 2,048 │ conv3_block2_3_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block2_add (Add) │ (None, 16, 16, 512) │ 0 │ conv3_block1_out[0][0… │ │ │ │ │ conv3_block2_3_bn[0][… │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block2_out │ (None, 16, 16, 512) │ 0 │ conv3_block2_add[0][0] │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block3_1_conv │ (None, 16, 16, 128) │ 65,664 │ conv3_block2_out[0][0] │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block3_1_bn │ (None, 16, 16, 128) │ 512 │ conv3_block3_1_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block3_1_relu │ (None, 16, 16, 128) │ 0 │ conv3_block3_1_bn[0][… │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block3_2_conv │ (None, 16, 16, 128) │ 147,584 │ conv3_block3_1_relu[0… │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block3_2_bn │ (None, 16, 16, 128) │ 512 │ conv3_block3_2_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block3_2_relu │ (None, 16, 16, 128) │ 0 │ conv3_block3_2_bn[0][… │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block3_3_conv │ (None, 16, 16, 512) │ 66,048 │ conv3_block3_2_relu[0… │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block3_3_bn │ (None, 16, 16, 512) │ 2,048 │ conv3_block3_3_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block3_add (Add) │ (None, 16, 16, 512) │ 0 │ conv3_block2_out[0][0… │ │ │ │ │ conv3_block3_3_bn[0][… │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block3_out │ (None, 16, 16, 512) │ 0 │ conv3_block3_add[0][0] │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block4_1_conv │ (None, 16, 16, 128) │ 65,664 │ conv3_block3_out[0][0] │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block4_1_bn │ (None, 16, 16, 128) │ 512 │ conv3_block4_1_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block4_1_relu │ (None, 16, 16, 128) │ 0 │ conv3_block4_1_bn[0][… │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block4_2_conv │ (None, 16, 16, 128) │ 147,584 │ conv3_block4_1_relu[0… │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block4_2_bn │ (None, 16, 16, 128) │ 512 │ conv3_block4_2_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block4_2_relu │ (None, 16, 16, 128) │ 0 │ conv3_block4_2_bn[0][… │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block4_3_conv │ (None, 16, 16, 512) │ 66,048 │ conv3_block4_2_relu[0… │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block4_3_bn │ (None, 16, 16, 512) │ 2,048 │ conv3_block4_3_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block4_add (Add) │ (None, 16, 16, 512) │ 0 │ conv3_block3_out[0][0… │ │ │ │ │ conv3_block4_3_bn[0][… │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv3_block4_out │ (None, 16, 16, 512) │ 0 │ conv3_block4_add[0][0] │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block1_1_conv │ (None, 8, 8, 256) │ 131,328 │ conv3_block4_out[0][0] │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block1_1_bn │ (None, 8, 8, 256) │ 1,024 │ conv4_block1_1_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block1_1_relu │ (None, 8, 8, 256) │ 0 │ conv4_block1_1_bn[0][… │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block1_2_conv │ (None, 8, 8, 256) │ 590,080 │ conv4_block1_1_relu[0… │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block1_2_bn │ (None, 8, 8, 256) │ 1,024 │ conv4_block1_2_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block1_2_relu │ (None, 8, 8, 256) │ 0 │ conv4_block1_2_bn[0][… │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block1_0_conv │ (None, 8, 8, 1024) │ 525,312 │ conv3_block4_out[0][0] │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block1_3_conv │ (None, 8, 8, 1024) │ 263,168 │ conv4_block1_2_relu[0… │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block1_0_bn │ (None, 8, 8, 1024) │ 4,096 │ conv4_block1_0_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block1_3_bn │ (None, 8, 8, 1024) │ 4,096 │ conv4_block1_3_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block1_add (Add) │ (None, 8, 8, 1024) │ 0 │ conv4_block1_0_bn[0][… │ │ │ │ │ conv4_block1_3_bn[0][… │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block1_out │ (None, 8, 8, 1024) │ 0 │ conv4_block1_add[0][0] │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block2_1_conv │ (None, 8, 8, 256) │ 262,400 │ conv4_block1_out[0][0] │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block2_1_bn │ (None, 8, 8, 256) │ 1,024 │ conv4_block2_1_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block2_1_relu │ (None, 8, 8, 256) │ 0 │ conv4_block2_1_bn[0][… │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block2_2_conv │ (None, 8, 8, 256) │ 590,080 │ conv4_block2_1_relu[0… │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block2_2_bn │ (None, 8, 8, 256) │ 1,024 │ conv4_block2_2_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block2_2_relu │ (None, 8, 8, 256) │ 0 │ conv4_block2_2_bn[0][… │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block2_3_conv │ (None, 8, 8, 1024) │ 263,168 │ conv4_block2_2_relu[0… │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block2_3_bn │ (None, 8, 8, 1024) │ 4,096 │ conv4_block2_3_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block2_add (Add) │ (None, 8, 8, 1024) │ 0 │ conv4_block1_out[0][0… │ │ │ │ │ conv4_block2_3_bn[0][… │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block2_out │ (None, 8, 8, 1024) │ 0 │ conv4_block2_add[0][0] │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block3_1_conv │ (None, 8, 8, 256) │ 262,400 │ conv4_block2_out[0][0] │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block3_1_bn │ (None, 8, 8, 256) │ 1,024 │ conv4_block3_1_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block3_1_relu │ (None, 8, 8, 256) │ 0 │ conv4_block3_1_bn[0][… │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block3_2_conv │ (None, 8, 8, 256) │ 590,080 │ conv4_block3_1_relu[0… │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block3_2_bn │ (None, 8, 8, 256) │ 1,024 │ conv4_block3_2_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block3_2_relu │ (None, 8, 8, 256) │ 0 │ conv4_block3_2_bn[0][… │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block3_3_conv │ (None, 8, 8, 1024) │ 263,168 │ conv4_block3_2_relu[0… │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block3_3_bn │ (None, 8, 8, 1024) │ 4,096 │ conv4_block3_3_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block3_add (Add) │ (None, 8, 8, 1024) │ 0 │ conv4_block2_out[0][0… │ │ │ │ │ conv4_block3_3_bn[0][… │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block3_out │ (None, 8, 8, 1024) │ 0 │ conv4_block3_add[0][0] │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block4_1_conv │ (None, 8, 8, 256) │ 262,400 │ conv4_block3_out[0][0] │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block4_1_bn │ (None, 8, 8, 256) │ 1,024 │ conv4_block4_1_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block4_1_relu │ (None, 8, 8, 256) │ 0 │ conv4_block4_1_bn[0][… │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block4_2_conv │ (None, 8, 8, 256) │ 590,080 │ conv4_block4_1_relu[0… │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block4_2_bn │ (None, 8, 8, 256) │ 1,024 │ conv4_block4_2_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block4_2_relu │ (None, 8, 8, 256) │ 0 │ conv4_block4_2_bn[0][… │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block4_3_conv │ (None, 8, 8, 1024) │ 263,168 │ conv4_block4_2_relu[0… │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block4_3_bn │ (None, 8, 8, 1024) │ 4,096 │ conv4_block4_3_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block4_add (Add) │ (None, 8, 8, 1024) │ 0 │ conv4_block3_out[0][0… │ │ │ │ │ conv4_block4_3_bn[0][… │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block4_out │ (None, 8, 8, 1024) │ 0 │ conv4_block4_add[0][0] │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block5_1_conv │ (None, 8, 8, 256) │ 262,400 │ conv4_block4_out[0][0] │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block5_1_bn │ (None, 8, 8, 256) │ 1,024 │ conv4_block5_1_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block5_1_relu │ (None, 8, 8, 256) │ 0 │ conv4_block5_1_bn[0][… │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block5_2_conv │ (None, 8, 8, 256) │ 590,080 │ conv4_block5_1_relu[0… │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block5_2_bn │ (None, 8, 8, 256) │ 1,024 │ conv4_block5_2_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block5_2_relu │ (None, 8, 8, 256) │ 0 │ conv4_block5_2_bn[0][… │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block5_3_conv │ (None, 8, 8, 1024) │ 263,168 │ conv4_block5_2_relu[0… │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block5_3_bn │ (None, 8, 8, 1024) │ 4,096 │ conv4_block5_3_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block5_add (Add) │ (None, 8, 8, 1024) │ 0 │ conv4_block4_out[0][0… │ │ │ │ │ conv4_block5_3_bn[0][… │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block5_out │ (None, 8, 8, 1024) │ 0 │ conv4_block5_add[0][0] │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block6_1_conv │ (None, 8, 8, 256) │ 262,400 │ conv4_block5_out[0][0] │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block6_1_bn │ (None, 8, 8, 256) │ 1,024 │ conv4_block6_1_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block6_1_relu │ (None, 8, 8, 256) │ 0 │ conv4_block6_1_bn[0][… │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block6_2_conv │ (None, 8, 8, 256) │ 590,080 │ conv4_block6_1_relu[0… │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block6_2_bn │ (None, 8, 8, 256) │ 1,024 │ conv4_block6_2_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block6_2_relu │ (None, 8, 8, 256) │ 0 │ conv4_block6_2_bn[0][… │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block6_3_conv │ (None, 8, 8, 1024) │ 263,168 │ conv4_block6_2_relu[0… │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block6_3_bn │ (None, 8, 8, 1024) │ 4,096 │ conv4_block6_3_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block6_add (Add) │ (None, 8, 8, 1024) │ 0 │ conv4_block5_out[0][0… │ │ │ │ │ conv4_block6_3_bn[0][… │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv4_block6_out │ (None, 8, 8, 1024) │ 0 │ conv4_block6_add[0][0] │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv5_block1_1_conv │ (None, 4, 4, 512) │ 524,800 │ conv4_block6_out[0][0] │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv5_block1_1_bn │ (None, 4, 4, 512) │ 2,048 │ conv5_block1_1_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv5_block1_1_relu │ (None, 4, 4, 512) │ 0 │ conv5_block1_1_bn[0][… │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv5_block1_2_conv │ (None, 4, 4, 512) │ 2,359,808 │ conv5_block1_1_relu[0… │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv5_block1_2_bn │ (None, 4, 4, 512) │ 2,048 │ conv5_block1_2_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv5_block1_2_relu │ (None, 4, 4, 512) │ 0 │ conv5_block1_2_bn[0][… │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv5_block1_0_conv │ (None, 4, 4, 2048) │ 2,099,200 │ conv4_block6_out[0][0] │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv5_block1_3_conv │ (None, 4, 4, 2048) │ 1,050,624 │ conv5_block1_2_relu[0… │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv5_block1_0_bn │ (None, 4, 4, 2048) │ 8,192 │ conv5_block1_0_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv5_block1_3_bn │ (None, 4, 4, 2048) │ 8,192 │ conv5_block1_3_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv5_block1_add (Add) │ (None, 4, 4, 2048) │ 0 │ conv5_block1_0_bn[0][… │ │ │ │ │ conv5_block1_3_bn[0][… │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv5_block1_out │ (None, 4, 4, 2048) │ 0 │ conv5_block1_add[0][0] │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv5_block2_1_conv │ (None, 4, 4, 512) │ 1,049,088 │ conv5_block1_out[0][0] │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv5_block2_1_bn │ (None, 4, 4, 512) │ 2,048 │ conv5_block2_1_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv5_block2_1_relu │ (None, 4, 4, 512) │ 0 │ conv5_block2_1_bn[0][… │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv5_block2_2_conv │ (None, 4, 4, 512) │ 2,359,808 │ conv5_block2_1_relu[0… │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv5_block2_2_bn │ (None, 4, 4, 512) │ 2,048 │ conv5_block2_2_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv5_block2_2_relu │ (None, 4, 4, 512) │ 0 │ conv5_block2_2_bn[0][… │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv5_block2_3_conv │ (None, 4, 4, 2048) │ 1,050,624 │ conv5_block2_2_relu[0… │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv5_block2_3_bn │ (None, 4, 4, 2048) │ 8,192 │ conv5_block2_3_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv5_block2_add (Add) │ (None, 4, 4, 2048) │ 0 │ conv5_block1_out[0][0… │ │ │ │ │ conv5_block2_3_bn[0][… │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv5_block2_out │ (None, 4, 4, 2048) │ 0 │ conv5_block2_add[0][0] │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv5_block3_1_conv │ (None, 4, 4, 512) │ 1,049,088 │ conv5_block2_out[0][0] │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv5_block3_1_bn │ (None, 4, 4, 512) │ 2,048 │ conv5_block3_1_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv5_block3_1_relu │ (None, 4, 4, 512) │ 0 │ conv5_block3_1_bn[0][… │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv5_block3_2_conv │ (None, 4, 4, 512) │ 2,359,808 │ conv5_block3_1_relu[0… │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv5_block3_2_bn │ (None, 4, 4, 512) │ 2,048 │ conv5_block3_2_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv5_block3_2_relu │ (None, 4, 4, 512) │ 0 │ conv5_block3_2_bn[0][… │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv5_block3_3_conv │ (None, 4, 4, 2048) │ 1,050,624 │ conv5_block3_2_relu[0… │ │ (Conv2D) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv5_block3_3_bn │ (None, 4, 4, 2048) │ 8,192 │ conv5_block3_3_conv[0… │ │ (BatchNormalization) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv5_block3_add (Add) │ (None, 4, 4, 2048) │ 0 │ conv5_block2_out[0][0… │ │ │ │ │ conv5_block3_3_bn[0][… │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ conv5_block3_out │ (None, 4, 4, 2048) │ 0 │ conv5_block3_add[0][0] │ │ (Activation) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ flatten_2 (Flatten) │ (None, 32768) │ 0 │ conv5_block3_out[0][0] │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ dense_6 (Dense) │ (None, 1024) │ 33,555,456 │ flatten_2[0][0] │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ dropout_3 (Dropout) │ (None, 1024) │ 0 │ dense_6[0][0] │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ dense_7 (Dense) │ (None, 3) │ 3,075 │ dropout_3[0][0] │ └───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘
Total params: 57,146,243 (218.00 MB)
Trainable params: 33,558,531 (128.02 MB)
Non-trainable params: 23,587,712 (89.98 MB)
In [ ]:
# Train the model
history = resnet_model.fit(
X_train, y_train,
validation_data=(X_val, y_val),
epochs=50,
batch_size=32
)
# Evaluate the model
loss, accuracy = resnet_model.evaluate(X_test, y_test, verbose=0)
print(f"Test Loss: {loss:.4f}")
print(f"Test Accuracy: {accuracy:.4f}")
Epoch 1/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 24s 24s/step - accuracy: 0.4762 - loss: 2.4244 - val_accuracy: 0.6000 - val_loss: 16.9759 Epoch 2/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 2s 2s/step - accuracy: 0.7143 - loss: 3.6958 - val_accuracy: 0.6000 - val_loss: 22.3862 Epoch 3/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 2s 2s/step - accuracy: 0.7143 - loss: 9.5107 - val_accuracy: 0.8000 - val_loss: 10.0144 Epoch 4/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 3s 3s/step - accuracy: 0.9524 - loss: 0.2967 - val_accuracy: 0.4000 - val_loss: 23.5436 Epoch 5/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 3s 3s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.2000 - val_loss: 70.2554 Epoch 6/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 3s 3s/step - accuracy: 0.9524 - loss: 2.9563 - val_accuracy: 0.2000 - val_loss: 87.2590 Epoch 7/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 4s 4s/step - accuracy: 0.9524 - loss: 0.6778 - val_accuracy: 0.2000 - val_loss: 71.8739 Epoch 8/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 3s 3s/step - accuracy: 1.0000 - loss: 7.3796e-08 - val_accuracy: 0.2000 - val_loss: 57.7418 Epoch 9/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 3s 3s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.6000 - val_loss: 55.1698 Epoch 10/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 3s 3s/step - accuracy: 1.0000 - loss: 0.0021 - val_accuracy: 0.6000 - val_loss: 55.2170 Epoch 11/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 3s 3s/step - accuracy: 0.9048 - loss: 2.6529 - val_accuracy: 0.6000 - val_loss: 51.0944 Epoch 12/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 2s 2s/step - accuracy: 1.0000 - loss: 5.6766e-09 - val_accuracy: 0.6000 - val_loss: 47.1808 Epoch 13/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 2s 2s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.6000 - val_loss: 43.5741 Epoch 14/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 2s 2s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.6000 - val_loss: 40.3052 Epoch 15/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 3s 3s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.6000 - val_loss: 37.3393 Epoch 16/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 2s 2s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.6000 - val_loss: 34.6401 Epoch 17/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 3s 3s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.6000 - val_loss: 32.1604 Epoch 18/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 2s 2s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.6000 - val_loss: 29.9319 Epoch 19/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 2s 2s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.6000 - val_loss: 27.9120 Epoch 20/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 2s 2s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.6000 - val_loss: 26.0629 Epoch 21/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 2s 2s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.6000 - val_loss: 24.3576 Epoch 22/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 3s 3s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.6000 - val_loss: 22.7897 Epoch 23/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 3s 3s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.6000 - val_loss: 21.3525 Epoch 24/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 4s 4s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.6000 - val_loss: 20.0368 Epoch 25/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 3s 3s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.6000 - val_loss: 18.8346 Epoch 26/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 2s 2s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.6000 - val_loss: 17.7306 Epoch 27/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 3s 3s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.6000 - val_loss: 16.7235 Epoch 28/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 3s 3s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.6000 - val_loss: 15.8012 Epoch 29/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 2s 2s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.6000 - val_loss: 15.0482 Epoch 30/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 2s 2s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.4000 - val_loss: 14.8606 Epoch 31/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 3s 3s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.4000 - val_loss: 14.8612 Epoch 32/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 3s 3s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.4000 - val_loss: 14.8770 Epoch 33/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 3s 3s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.4000 - val_loss: 14.9052 Epoch 34/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 3s 3s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.4000 - val_loss: 14.9492 Epoch 35/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 4s 4s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.6000 - val_loss: 15.0045 Epoch 36/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 2s 2s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.6000 - val_loss: 15.0666 Epoch 37/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 3s 3s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.6000 - val_loss: 15.1318 Epoch 38/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 3s 3s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.6000 - val_loss: 15.1979 Epoch 39/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 4s 4s/step - accuracy: 0.9524 - loss: 1.8281 - val_accuracy: 0.4000 - val_loss: 18.4035 Epoch 40/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 3s 3s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.4000 - val_loss: 21.6966 Epoch 41/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 2s 2s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.6000 - val_loss: 24.9272 Epoch 42/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 2s 2s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.6000 - val_loss: 28.9622 Epoch 43/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 3s 3s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.6000 - val_loss: 32.6679 Epoch 44/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 4s 4s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.6000 - val_loss: 36.0253 Epoch 45/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 3s 3s/step - accuracy: 0.9524 - loss: 0.3017 - val_accuracy: 0.6000 - val_loss: 30.5192 Epoch 46/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 2s 2s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.6000 - val_loss: 25.6555 Epoch 47/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 2s 2s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.6000 - val_loss: 21.2863 Epoch 48/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 4s 4s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.6000 - val_loss: 17.4278 Epoch 49/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 2s 2s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.4000 - val_loss: 14.1676 Epoch 50/50 1/1 ━━━━━━━━━━━━━━━━━━━━ 3s 3s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.4000 - val_loss: 11.5948 Test Loss: 162.8343 Test Accuracy: 0.0000
In [ ]:
# Plot training & validation accuracy values
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
# Plot training & validation loss values
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
